Project_V1

suppressPackageStartupMessages(library(readxl))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(plotly))
suppressPackageStartupMessages(library(fmsb))

data = read_excel("Project_1_Data.xlsx", sheet = "pooled123")

filteredData = select(data, Date, PID, BSSQ_1:BSSQ_16, ASSQ_1:ASSQ_16, age, VRexperience, ssq_full, gender, sm)
#we only want to look at those that did not undergo social modelling conditions
filteredData = subset(filteredData, sm == "NO_SM")

#calculating differences between baseline and active SSQ for each symptom
filteredData = mutate(filteredData, d_discomfort = ASSQ_1 - BSSQ_1)
filteredData = mutate(filteredData, d_fatigue = ASSQ_2 - BSSQ_2)
filteredData = mutate(filteredData, d_headache = ASSQ_3 - BSSQ_3)
filteredData = mutate(filteredData, d_eyestrain = ASSQ_4 - BSSQ_4)
filteredData = mutate(filteredData, d_difficulty_focusing = ASSQ_5 - BSSQ_5)
filteredData = mutate(filteredData, d_salivation = ASSQ_6 - BSSQ_6)
filteredData = mutate(filteredData, d_sweating = ASSQ_7 - BSSQ_7)
filteredData = mutate(filteredData, d_nausea = ASSQ_8 - BSSQ_8)
filteredData = mutate(filteredData, d_difficulty_concentrating = ASSQ_9 - BSSQ_9)
filteredData = mutate(filteredData, d_fullness_of_head = ASSQ_10 - BSSQ_10)
filteredData = mutate(filteredData, d_blurred_vision = ASSQ_11 - BSSQ_11)
filteredData = mutate(filteredData, d_dizziness_o = ASSQ_12 - BSSQ_12)
filteredData = mutate(filteredData, d_dizziness_c = ASSQ_13 - BSSQ_13)
filteredData = mutate(filteredData, d_vertigo = ASSQ_14 - BSSQ_14)
filteredData = mutate(filteredData, d_stomach_awareness = ASSQ_15 - BSSQ_15)
filteredData = mutate(filteredData, d_burping = ASSQ_16 - BSSQ_16)


#reclasss VR experience as factor (was chr)
filteredData$VRexperience = as.factor(filteredData$VRexperience)

#we want to filter this data even further and split it into age groups
#once in age groups, calculate the mean change for each of the age groups for each symptom
filteredData = mutate(filteredData, age_group = case_when(
  age >= 16 & age <= 21 ~ "16 to 21",
  age >= 22 & age <= 29 ~ "22 to 29",
  age >= 30 & age <= 37 ~ "30 to 37",
  age >= 38 & age <= 45 ~ "38 to 45",
  age > 45 ~ "above 45"
))

#convert the age groups into factors
filteredData$age_group = as.factor(filteredData$age_group)

#renaming columns to be more informative
names(filteredData)[names(filteredData) == 'BSSQ_1'] <- 'BSSQ_discomfort'
names(filteredData)[names(filteredData) == 'BSSQ_2'] <- 'BSSQ_fatigue'
names(filteredData)[names(filteredData) == 'BSSQ_3'] <- 'BSSQ_headache'
names(filteredData)[names(filteredData) == 'BSSQ_4'] <- 'BSSQ_eyestrain'
names(filteredData)[names(filteredData) == 'BSSQ_5'] <- 'BSSQ_difficulty_focusing'
names(filteredData)[names(filteredData) == 'BSSQ_6'] <- 'BSSQ_salivation'
names(filteredData)[names(filteredData) == 'BSSQ_7'] <- 'BSSQ_sweating'
names(filteredData)[names(filteredData) == 'BSSQ_8'] <- 'BSSQ_nausea'
names(filteredData)[names(filteredData) == 'BSSQ_9'] <- 'BSSQ_difficulty_concentrating'
names(filteredData)[names(filteredData) == 'BSSQ_10'] <- 'BSSQ_fullness_of_head'
names(filteredData)[names(filteredData) == 'BSSQ_11'] <- 'BSSQ_blurred_vision'
names(filteredData)[names(filteredData) == 'BSSQ_12'] <- 'BSSQ_dizziness_o'
names(filteredData)[names(filteredData) == 'BSSQ_13'] <- 'BSSQ_dizziness_c'
names(filteredData)[names(filteredData) == 'BSSQ_14'] <- 'BSSQ_vertigo'
names(filteredData)[names(filteredData) == 'BSSQ_15'] <- 'BSSQ_stomach_awareness'
names(filteredData)[names(filteredData) == 'BSSQ_16'] <- 'BSSQ_burping'





names(filteredData)[names(filteredData) == 'ASSQ_1'] <- 'ASSQ_discomfort'
names(filteredData)[names(filteredData) == 'ASSQ_2'] <- 'ASSQ_fatigue'
names(filteredData)[names(filteredData) == 'ASSQ_3'] <- 'ASSQ_headache'
names(filteredData)[names(filteredData) == 'ASSQ_4'] <- 'ASSQ_eyestrain'
names(filteredData)[names(filteredData) == 'ASSQ_5'] <- 'ASSQ_difficulty_focusing'
names(filteredData)[names(filteredData) == 'ASSQ_6'] <- 'ASSQ_salivation'
names(filteredData)[names(filteredData) == 'ASSQ_7'] <- 'ASSQ_sweating'
names(filteredData)[names(filteredData) == 'ASSQ_8'] <- 'ASSQ_nausea'
names(filteredData)[names(filteredData) == 'ASSQ_9'] <- 'ASSQ_difficulty_concentrating'
names(filteredData)[names(filteredData) == 'ASSQ_10'] <- 'ASSQ_fullness_of_head'
names(filteredData)[names(filteredData) == 'ASSQ_11'] <- 'ASSQ_blurred_vision'
names(filteredData)[names(filteredData) == 'ASSQ_12'] <- 'ASSQ_dizziness_o'
names(filteredData)[names(filteredData) == 'ASSQ_13'] <- 'ASSQ_dizziness_c'
names(filteredData)[names(filteredData) == 'ASSQ_14'] <- 'ASSQ_vertigo'
names(filteredData)[names(filteredData) == 'ASSQ_15'] <- 'ASSQ_stomach_awareness'
names(filteredData)[names(filteredData) == 'ASSQ_16'] <- 'ASSQ_burping'


#isolating the data dictionary
data_dict = read_excel("Project_1_Data.xlsx", sheet = "data_dictionary")

#filter into groups with experience or not
withVRexperience = filter(filteredData, VRexperience == 'Yes')
noVRexperience = filter(filteredData, VRexperience == 'No')

#taking averages for with/without experience
yes_avg_d_discomfort = mean(withVRexperience$d_discomfort)
yes_avg_d_fatigue = mean(withVRexperience$d_fatigue)
yes_avg_d_headache = mean(withVRexperience$d_headache)
yes_avg_d_eyestrain = mean(withVRexperience$d_eyestrain)
yes_avg_d_difficulty_focusing = mean(withVRexperience$d_difficulty_focusing)
yes_avg_d_salivation = mean(withVRexperience$d_salivation)
yes_avg_d_sweating = mean(withVRexperience$d_sweating)
yes_avg_d_nausea = mean(withVRexperience$d_nausea)
yes_avg_d_difficulty_concentrating = mean(withVRexperience$d_difficulty_concentrating)
yes_avg_d_fullness = mean(withVRexperience$d_fullness_of_head)
yes_avg_d_vision = mean(withVRexperience$d_blurred_vision)
yes_avg_d_dizziness_o = mean(withVRexperience$d_dizziness_o)
yes_avg_d_dizziness_c = mean(withVRexperience$d_dizziness_c)
yes_avg_d_vertigo = mean(withVRexperience$d_vertigo)
yes_avg_d_stomach = mean(withVRexperience$d_stomach_awareness)
yes_avg_d_burping = mean(withVRexperience$d_burping)


no_avg_d_discomfort = mean(noVRexperience$d_discomfort)
no_avg_d_fatigue = mean(noVRexperience$d_fatigue)
no_avg_d_headache = mean(noVRexperience$d_headache)
no_avg_d_eyestrain = mean(noVRexperience$d_eyestrain)
no_avg_d_difficulty_focusing = mean(noVRexperience$d_difficulty_focusing)
no_avg_d_salivation = mean(noVRexperience$d_salivation)
no_avg_d_sweating = mean(noVRexperience$d_sweating)
no_avg_d_nausea = mean(noVRexperience$d_nausea)
no_avg_d_difficulty_concentrating = mean(noVRexperience$d_difficulty_concentrating)
no_avg_d_fullness = mean(noVRexperience$d_fullness_of_head)
no_avg_d_vision = mean(noVRexperience$d_blurred_vision)
no_avg_d_dizziness_o = mean(noVRexperience$d_dizziness_o)
no_avg_d_dizziness_c = mean(noVRexperience$d_dizziness_c)
no_avg_d_vertigo = mean(noVRexperience$d_vertigo)
no_avg_d_stomach = mean(noVRexperience$d_stomach_awareness)
no_avg_d_burping = mean(noVRexperience$d_burping)


#taking averages for different age groups
grp1 = filter(filteredData, age_group == '16 to 21')
grp2 = filter(filteredData, age_group == '22 to 29')
grp3 = filter(filteredData, age_group == '30 to 37')
grp4 = filter(filteredData, age_group == '38 to 45')
grp5 = filter(filteredData, age_group == 'above 45')

mean_sqq_ages = c(mean(grp1$ssq_full),
                  mean(grp2$ssq_full),
                  mean(grp3$ssq_full),
                  mean(grp4$ssq_full),
                  mean(grp5$ssq_full)
                  )

data_by_age_group = data.frame(
  age_group = c("16 to 21", '22 to 29','30 to 37','38 to 45','Above 45'),
  mean_ssq = mean_sqq_ages
  
)

filteredData = mutate(filteredData, "generation" = case_when(year(Date) - age >= 2010 ~ "a", year(Date) - age >= 1997 ~ "Z", year(Date) - age >= 1981 ~ "Y", year(Date) - age >= 1965 ~ "X", TRUE ~ "Old"))

Summary of Findings

(executive summary goes here)

Initial Data Analysis (IDA)

Source

Our data was sourced from Cosette Saunder’s paper “Socially Acquired Nocebo Effects Generalize but Are Not Attenuated by Choice”.

Structure

We specifically looked at participants that did not undego any social modelling; 69 participants, each with 51 variables recorded. Our project focused the following variables:

• Baseline SSQ (BSSQ), Active SSQ (ASSQ), and SSQ_Fullof 16 symptoms (quantitative, discrete): self-reported symptom severity before and after undergoing VR respectively, on a scale of 1 to 10.

• Participants’ VRexperience (qualitative, nominal); sorted by experience to see how symptoms differed.

• age of participants (quantitative, discrete); they were then sorted into age groups.

• The change (\(\Delta\)) between BSSQ and ASSQ was calculated for each participant for each symptom, and we took the average \(\Delta\) for each symptom for each group (VRexperience and age_group) (quantitative, discrete).

Age Groups

data_age_groups = select(filteredData, age_group)
groups_counted = data_age_groups %>% count(age_group)

age_pie = plot_ly(groups_counted, labels = ~age_group, values = ~n,
                 type = 'pie', direction = 'clockwise', sort = FALSE, rotation = 30)
age_pie <- age_pie %>% layout(title = 'Distribution of ages',
                            showlegend = TRUE)
age_pie

VR Experience

data_experience = select(filteredData, VRexperience)
exp_counted = data_experience %>% count(VRexperience)

vr_pie = plot_ly(exp_counted, labels = ~VRexperience, values = ~n,
                 type = 'pie')
vr_pie <- vr_pie %>% layout(title = 'Distribution of VR experience',
                            showlegend = TRUE)

vr_pie

VR Experience Age Groups

data_experience = select(withVRexperience, age_group)
exp_counted = data_experience %>% count(age_group)

vr_pie = plot_ly(exp_counted, labels = ~age_group, values = ~n,
                 type = 'pie')
vr_pie <- vr_pie %>% layout(title = 'Distribution of age groups for those with VR experience',
                            showlegend = TRUE)

vr_pie

No Experience Age Groups

data_experience = select(noVRexperience, age_group)
exp_counted = data_experience %>% count(age_group)

vr_pie = plot_ly(exp_counted, labels = ~age_group, values = ~n,
                 type = 'pie')
vr_pie <- vr_pie %>% layout(title = 'Distribution of age groups for those without VR experience',
                            showlegend = TRUE)

vr_pie

Limitations

• Self-reporting bias for both BSSQ and ASSQ which makes the values prone to being over or underestimates by each participant.

• VRexperience is a binary qualitative classification, it is not descriptive of the nature, amount or frequency, and that those with more than 10 VR experiences were excluded.

Research Question

What is the effect of past VR experience on the symptoms experienced by people?

plt = ggplot(filteredData, aes(x = VRexperience, y = ssq_full, fill = VRexperience)) +
  geom_boxplot() +
  theme_minimal() +
  labs(x = "VR Experience", y = "Full SSQ")
ggplotly(plt)

Statistical Analysis

Those with and without VR experience reported varied severity of symptoms. Interestingly, the VR experienced group saw a greater median ssq_full, at 7.0 compared to -0.5 for those without experience, indicating those with experience reported more severe symptoms afterwards. VR experienced participants also showed a greater IQR than those without, at 16.25 and 8.5 respectively, pointing to experienced users reporting less consistent symptom severity. That is, the difference in average rating from the Simulator Sickness Questionnaire (SSQ) is consistently lower for less experienced users. With respect to age, the scatterplot we have has a very low correlation of [INSERT CORRELATION FOR YES AND NO EXPERIECNE CALCULATION HERE FUCK R STUDIO], implying that the data will not fit to a linear model, and there conclusively exists no correlation between age and severity of symptoms, across both having some and zero past experience with VR.

library(plotly)

yes_means = c(yes_avg_d_discomfort,
              yes_avg_d_fatigue,
              yes_avg_d_headache,
              yes_avg_d_eyestrain,
              yes_avg_d_sweating,
              yes_avg_d_nausea,
              yes_avg_d_dizziness_c,
              yes_avg_d_dizziness_o,
              yes_avg_d_difficulty_focusing,
              yes_avg_d_difficulty_concentrating,
              yes_avg_d_salivation,
              yes_avg_d_vision,
              yes_avg_d_vertigo,
              yes_avg_d_stomach,
              yes_avg_d_fullness,
              yes_avg_d_burping
              )

no_means = c(no_avg_d_discomfort,
             no_avg_d_fatigue,
             no_avg_d_headache,
             no_avg_d_eyestrain,
             no_avg_d_sweating,
             no_avg_d_nausea,
             no_avg_d_dizziness_c,
             no_avg_d_dizziness_o,
             no_avg_d_difficulty_focusing,
             no_avg_d_difficulty_concentrating,
             no_avg_d_salivation,
             no_avg_d_vision,
             no_avg_d_vertigo,
             no_avg_d_stomach,
             no_avg_d_fullness,
             no_avg_d_burping
             )

fig <- plot_ly(
  type  = 'scatterpolar',
  fill = 'toself',
  title = "Average change in BSSQ and ASSQ depending on VR experience for each symptom"
)

fig <- fig %>% 
  add_trace(
    r = yes_means,
    theta = c('Discomfort', 'Fatigue', 'Headache', 'Eyestrain', 'Sweating', 'Nausea', 'Dizziness (closed)', 'Dizziness (open)', 'Difficulty Focusing', 'Difficulty Concentrating', 'Salivation', 'Blurry Vision', 'Vertigo', 'Stomach Awareness', 'Fullness of head','Burping'),
    name = 'With VR Experience'
  )

fig <- fig %>% 
  add_trace(
    r = no_means,
    theta = c('Discomfort', 'Fatigue', 'Headache', 'Eyestrain', 'Sweating', 'Nausea','Dizziness (closed)', 'Dizziness (open)', 'Difficulty Focusing', 'Difficulty Concentrating', 'Salivation', 'Blurry Vision',  'Vertigo', 'Stomach Awareness', 'Fullness of head','Burping'),
    name = 'No VR Experience'
  )

fig <- fig %>%
  layout(
    polar = list(
      radialaxis = list(
        visible = T,
        range = c(-0.5,2)
      )
    ),
    showlegend = T
  )

fig

The spider-chart above reinforces what we see in the box-plot, with VR experienced users all indicating a higher average \(\Delta\) for almost all of the 16 symptoms.

To further investigate why our conclusions contradicts research, we investigate a potential confounding variable: age.

With Experience

plt2 = ggplot(withVRexperience, aes(x = age_group, y = ssq_full, fill = age_group)) +
  geom_boxplot() +
  labs(title = "Full SSQ for each age group (With experience)", y = "Full SSQ", x = "Age Group")

ggplotly(plt2)

No Experience

plt2 = ggplot(noVRexperience, aes(x = age_group, y = ssq_full, fill = age_group)) +
  geom_boxplot() +
  labs(title = "Full SSQ Scores for each age group (No experience)", y = "Full SSQ", x = "Age Group")

ggplotly(plt2)

Comparison

library(tidyverse)
library(plotly)

plt = ggplot(filteredData, aes(x = as.factor(age_group) , y = ssq_full, group = VRexperience, fill = VRexperience)) +
  geom_boxplot(position = "dodge") +
  labs(x = "Age Group",  y = "Full SSQ", title = "Full SSQ Scores by Age Group (both groups)")

ggplotly(plt) %>%  layout(boxmode = "group")

Combined

plt2 = ggplot(filteredData, aes(x = age_group, y = ssq_full, fill = age_group)) +
  geom_boxplot() +
  labs(title = "Full SSQ Scores for each age group", y = "Full SSQ", x = "Age Group")

ggplotly(plt2)

Source Analysis

These observations are in contrast to articles published by IEEE Games such as [1] - ‘Motion Sickness in Virtual Reality: An Empirical Evaluation’, stating that “VR users having VR experience or 3D game experience or motion sickness experience will undergo less motion sickness…” Justification for the confounding results lies in the smaller sample space, wherein it is given that each person’s experience will vary too greatly for a strong correlation to be found across results in different papers. Furthermore, the observations found here conflict with Heesok Oh and Wookho Son’s [2] ‘Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study’, where their “results indicate that the elder participants experienced 393% more severe nausea”, as opposed to our data set which depicts no relation between varying symptoms and age.

ggplot(filteredData) +
  geom_boxplot(aes(x = "Discomfort", y = d_discomfort, fill = VRexperience)) +
  geom_boxplot(aes(x = "Fatigue", y = d_fatigue, fill = VRexperience)) +
  geom_boxplot(aes(x = "Headache", y = d_headache, fill = VRexperience)) +
  geom_boxplot(aes(x = "Eyestrain", y = d_eyestrain, fill = VRexperience)) +
  geom_boxplot(aes(x = "Difficulty Focusing", y = d_difficulty_focusing, fill = VRexperience)) +
  geom_boxplot(aes(x = "Salivation", y = d_salivation, fill = VRexperience)) +
  geom_boxplot(aes(x = "Sweating", y = d_sweating, fill = VRexperience)) +
  geom_boxplot(aes(x = "Nausea", y = d_nausea, fill = VRexperience)) +
  geom_boxplot(aes(x = "Difficulty Concentrating", y = d_difficulty_concentrating, fill = VRexperience)) +
  geom_boxplot(aes(x = "Fullness of Head", y = d_fullness_of_head, fill = VRexperience)) +
  geom_boxplot(aes(x = "Blurred Vision", y = d_blurred_vision, fill = VRexperience)) +
  geom_boxplot(aes(x = "Dizziness (o)", y = d_dizziness_o, fill = VRexperience)) +
  geom_boxplot(aes(x = "Dizziness (c)", y = d_dizziness_c, fill = VRexperience)) +
  geom_boxplot(aes(x = "Vertigo", y = d_vertigo, fill = VRexperience)) +
  geom_boxplot(aes(x = "Stomach Awareness", y = d_stomach_awareness, fill = VRexperience)) +
  theme_minimal() +
  labs(title = "VR Experience and Symptom Change",
       x = "Symptom",
       y = "Change in Severity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

library(tidyverse)
library(plotly)

plt = ggplot(filteredData, aes(x = as.factor(gender) , y = ssq_full, group = VRexperience, fill = VRexperience)) +
  geom_boxplot(position = "dodge") +
  labs(x = "gender",  y = "Full SSQ")
plt

ggplotly(plt) %>%  layout(boxmode = "group")

Warning: 'layout' objects don't have these attributes: 'boxmode'
Valid attributes include:
'_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'smith', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'

Potential Justification

-reason out in short sentences possible causes for why age doesnt correlate (ie smaller sample space)

- other issues might also lie in how reporting sickness is inherently clunky (like wtf is the burping questionnare bro come on)

-might try and explain comical amounts of box plots that are just a single line (challenge problem for me to answer if i dare)

library(plotly)
library(tidyselect)

mean_sqq_ages = c(mean(grp1$ssq_full),
                  mean(grp2$ssq_full),
                  mean(grp3$ssq_full),
                  mean(grp4$ssq_full),
                  mean(grp5$ssq_full)
                  )

fig2 <- plot_ly(
  type = 'scatterpolar',
  fill = 'toself',
  r = mean_sqq_ages,
  theta = c("16 to 21",
            "22 to 29",
            "30 to 37",
            "38 to 45",
            "above 45")
)
fig2 <- fig2 %>%
  layout(
    polar = list(
      radialaxis = list(
        visible = T,
        range = c(0,20)
      )
    ),
    showlegend = T
  )

fig2

No scatterpolar mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

fig3 = ggplot(data_by_age_group, aes(x = age_group, y = mean_ssq)) +
  geom_bar(stat = 'identity')



ggplotly(fig3)

library(tidyverse)

p = ggplot(filteredData, aes(x = age, y = ssq_full, colour = VRexperience)) +
  geom_point()

p

plt = ggplot(filteredData, aes(x = VRexperience, y = ssq_full, fill = VRexperience)) +
  geom_boxplot() +
  theme_minimal()

ggplotly(plt)

plt2 = ggplot(filteredData, aes(x = age_group, y = ssq_full)) +
  geom_boxplot()

ggplotly(plt2)

library(tidyverse)

yex = filter(filteredData, VRexperience == "Yes")
nox = filter(filteredData, VRexperience == "No")

q1 = quantile(yex$age, probs = c(0.25), na.rm = TRUE) |> as.numeric()
q2 = quantile(nox$age, probs = c(0.25), na.rm = TRUE) |> as.numeric()
# https://forum.posit.co/t/make-the-r-function-quantile-return-a-numeric-value/178084
q3 = 2*q2-q1

ggplot(filteredData, aes(x = VRexperience, y = age)) + geom_boxplot() + geom_hline(yintercept = q1) + geom_hline(yintercept = q2) + geom_hline(yintercept = q3)

filteredData = mutate(filteredData, age_class = case_when(age > q3 ~ "older",
                                                      age > q2 ~ "old",
                                                      age > q1 ~ "young",
                                                      age > 0 ~ "younger"))

ggplot(filter(filteredData, age_class == "old" | age_class == "young"), aes(x = age_class, y = ssq_full)) + geom_boxplot()

filteredData["age_group"] = cut(filteredData$age, c(16, 22, 30, 38, 46, Inf), c("16-21", "22-29", "30-37", "38-45", "45+"), include.lowest = TRUE)

ggplot(filteredData, aes(x = VRexperience, y = ssq_full, fill = VRexperience)) + stat_summary(fun = "mean", geom = "bar", position = "dodge")

library(lubridate)

class(filteredData$Date[1])

[1] "POSIXct" "POSIXt" 

d = filteredData$Date[1] |> as.POSIXct()
year(d) # used chatgpt for this because there is no known website in the universe that has this information for SOME REASON

[1] 2021

ggplot(filteredData, aes(x = generation, y = ssq_full)) + geom_boxplot()

ggplot(filteredData, aes(x = age, y = ssq_full, color = generation)) + geom_point()

Professional Standard of Report

Acknowledgements

References

https://ieeexplore.ieee.org/abstract/document/9133071 [1]

https://www.mdpi.com/1424-8220/22/4/1314 [2]